TY - JOUR
T1 - Assessing resolvability, parsability, and consistency of RDF resources
T2 - a use case in rare diseases
AU - Zhang, Shuxin
AU - Benis, Nirupama
AU - Cornet, Ronald
N1 - Funding Information: We appreciate the funding support from the European Joint Programme on Rare Diseases (EJP RD), as well as Rajaram Kaliyaperumal and Philip van Damme’s testing of the tool. Funding Information: This work was supported by funding from the European Union’s Horizon 2020 research and innovation programme under the EJP RD COFUND-EJP N 825575. The funder has had no involvement in the design of the study, nor in the collection, analysis, or interpretation of data, or in writing the manuscript. Publisher Copyright: © 2023, The Author(s).
PY - 2023/12/1
Y1 - 2023/12/1
N2 - Introduction: Healthcare data and the knowledge gleaned from it play a key role in improving the health of current and future patients. These knowledge sources are regularly represented as ‘linked’ resources based on the Resource Description Framework (RDF). Making resources ‘linkable’ to facilitate their interoperability is especially important in the rare-disease domain, where health resources are scattered and scarce. However, to benefit from using RDF, resources need to be of good quality. Based on existing metrics, we aim to assess the quality of RDF resources related to rare diseases and provide recommendations for their improvement. Methods: Sixteen resources of relevance for the rare-disease domain were selected: two schemas, three metadatasets, and eleven ontologies. These resources were tested on six objective metrics regarding resolvability, parsability, and consistency. Any URI that failed the test based on any of the six metrics was recorded as an error. The error count and percentage of each tested resource were recorded. The assessment results were represented in RDF, using the Data Quality Vocabulary schema. Results: For three out of the six metrics, the assessment revealed quality issues. Eleven resources have non-resolvable URIs with proportion to all URIs ranging from 0.1% (6/6,712) in the Anatomical Therapeutic Chemical Classification to 13.7% (17/124) in the WikiPathways Ontology; seven resources have undefined URIs; and two resources have incorrectly used properties of the ‘owl:ObjectProperty’ type. Individual errors were examined to generate suggestions for the development of high-quality RDF resources, including the tested resources. Conclusion: We assessed the resolvability, parsability, and consistency of RDF resources in the rare-disease domain, and determined the extent of these types of errors that potentially affect interoperability. The qualitative investigation on these errors reveals how they can be avoided. All findings serve as valuable input for the development of a guideline for creating high-quality RDF resources, thereby enhancing the interoperability of biomedical resources.
AB - Introduction: Healthcare data and the knowledge gleaned from it play a key role in improving the health of current and future patients. These knowledge sources are regularly represented as ‘linked’ resources based on the Resource Description Framework (RDF). Making resources ‘linkable’ to facilitate their interoperability is especially important in the rare-disease domain, where health resources are scattered and scarce. However, to benefit from using RDF, resources need to be of good quality. Based on existing metrics, we aim to assess the quality of RDF resources related to rare diseases and provide recommendations for their improvement. Methods: Sixteen resources of relevance for the rare-disease domain were selected: two schemas, three metadatasets, and eleven ontologies. These resources were tested on six objective metrics regarding resolvability, parsability, and consistency. Any URI that failed the test based on any of the six metrics was recorded as an error. The error count and percentage of each tested resource were recorded. The assessment results were represented in RDF, using the Data Quality Vocabulary schema. Results: For three out of the six metrics, the assessment revealed quality issues. Eleven resources have non-resolvable URIs with proportion to all URIs ranging from 0.1% (6/6,712) in the Anatomical Therapeutic Chemical Classification to 13.7% (17/124) in the WikiPathways Ontology; seven resources have undefined URIs; and two resources have incorrectly used properties of the ‘owl:ObjectProperty’ type. Individual errors were examined to generate suggestions for the development of high-quality RDF resources, including the tested resources. Conclusion: We assessed the resolvability, parsability, and consistency of RDF resources in the rare-disease domain, and determined the extent of these types of errors that potentially affect interoperability. The qualitative investigation on these errors reveals how they can be avoided. All findings serve as valuable input for the development of a guideline for creating high-quality RDF resources, thereby enhancing the interoperability of biomedical resources.
KW - Linked data
KW - Quality assessment
KW - RDF
KW - Rare disease
UR - http://www.scopus.com/inward/record.url?scp=85178850202&partnerID=8YFLogxK
U2 - https://doi.org/10.1186/s13326-023-00299-3
DO - https://doi.org/10.1186/s13326-023-00299-3
M3 - Article
C2 - 38053130
SN - 2041-1480
VL - 14
JO - Journal of Biomedical Semantics
JF - Journal of Biomedical Semantics
IS - 1
M1 - 19
ER -